Mathematical Model May Improve Cancer Drugs

A UT Dallas interdisciplinary researcher has launched a human genome analysis project intended to create a mathematical model that improves the efficacy of cancer drugs while reducing their manufacturing costs by as much as 30 percent.

“What I propose to do is to find mathematical models of how a cross section of cancer patients would react to a particular clinical trial drug,” he said. “If it is likely that an unacceptably high fraction would react adversely, that would allow the developers to kill that program in a timely manner.”

Sagar’s statistical method would reduce the size of the massive databases that store genetic data. With his model, clinicians would be able to sub-sample those databases while still retaining inherent statistical features such as correlations, thus saving time and money without sacrificing accuracy.

The Cecil and Ida Green Professor of Systems Biology Science, Sagar sees statistical analysis of individual genomes as a way to bridge the gap between medicine’s current understanding of cause-and-effect relationships between genetic makeup and a patient’s responsiveness to treatment.

“There is wide variation both in beneficial and adverse effects of medications,” he said. “It is believed that DNA variations can be used to explain those differences.”

“If a pharmaceutical company starts with 100 candidates, a typical number, statistical methods can say ‘Out of this lot, these 30 are sure to fail,’” Sagar said. “But that does not mean that the remaining candidates are guaranteed to succeed. By killing potential failures early, though, the overall cost of drug development can be reduced by about 30 percent, in my estimate.”

The same method that allows for a reduction in costs can also allow drug treatments to become more personalized with what Sagar calls small-batch drug manufacturing. If cancer patients were given slight variations of a drug, ones that were customized for the idiosyncrasies of their individual genomes, they would be able to avoid having to deal with unnecessary – and often dangerous – side effects often associated with a one-size-fits-all treatment approach. Even so, Sagar isn’t proposing a statistical magic bullet for an eventual cancer cure.

“When the draft human genome was first published in February 2001, it was believed that by analyzing the DNA of individuals, finding the variations and then doing massive statistical correlations, it would be possible to find out which individuals were susceptible to which disease,” he said. “The reality seems to be that the environment is still more important than genetics.”